Application of artificial intelligence in distinguishing genuine microseismic events from the noise signals in underground mines

Abstract

The discrimination of genuine microseismic events from the noise signals during microseismic monitoring in underground mines is critical to prevent misinterpretations and correctly detect the highly stressed zones prone to rockbursting. This study proposes a novel mathematical classifier using genetic programming (GP) algorithm to distinguish the recorded signals in a coal mine. A database containing 100 recorded signals and six parameters representing the spectrum and waveform characteristics of the signals was employed for the modeling task. The hyperparameter tuning was conducted through a systematic analysis to find the best GP classifier. The classification performance of the GP model was compared with that of the linear discriminant analysis (LDA) technique based on several statistical measures. By developing an explicit mathematical model, the GP algorithm opened the complex nature of the existing machine learning-based classifiers and showed a higher classification accuracy than LDA. The proposed model in this study can be easily used to detect genuine microseismic events and will help the engineers apply the necessary controlling techniques to mitigate the occurrence probability of catastrophic hazards.